Real-time dynamic noise reduction using convolutional networks

A method for dynamic noise reduction, comprising:

• receiving, by an encoder, an input audio signal, the input audio signal including voice;
• performing, by the encoder, a short-time Fourier transform on the audio signal to generate a first magnitude spectrum and a phase spectrum;
• estimating, by a temporal convolution network (TCN), a separation mask based on the first magnitude spectrum using deep learning, wherein the TCN comprises non-causal convolution layers merged with causal convolution layers;
• and
• mixing the separation mask with the first magnitude spectrum to separate the voice from the input audio signal and obtain a second magnitude spectrum for the voice.

At least one non-transitory computer readable medium, comprising a set of instructions, which when executed by one or more computing devices, cause the one or more computing devices to:

• receive, by an encoder, an input audio signal, the input audio signal including voice;
• perform, by the encoder, a short-time Fourier transform on the audio signal to generate a first magnitude spectrum and a phase spectrum;
• estimate, by a temporal convolution network (TCN), a separation mask based on the first magnitude spectrum using deep learning, wherein the TCN comprises non-causal convolution layers merged with causal convolution layers;
• and
• mix the separation mask with the first magnitude spectrum to generate a second magnitude spectrum for the voice.